Overview

Dataset statistics

Number of variables23
Number of observations702
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory147.8 KiB
Average record size in memory215.6 B

Variable types

Text2
DateTime1
Categorical6
Numeric14

Alerts

Abdominal Circumference (cm) is highly overall correlated with Waist-to-Height RatioHigh correlation
BMI is highly overall correlated with CVD Risk Score and 1 other fieldsHigh correlation
CVD Risk Score is highly overall correlated with BMIHigh correlation
Estimated LDL (mg/dL) is highly overall correlated with Total Cholesterol (mg/dL)High correlation
Height (cm) is highly overall correlated with Height (m)High correlation
Height (m) is highly overall correlated with Height (cm)High correlation
Total Cholesterol (mg/dL) is highly overall correlated with Estimated LDL (mg/dL)High correlation
Waist-to-Height Ratio is highly overall correlated with Abdominal Circumference (cm)High correlation
Weight (kg) is highly overall correlated with BMIHigh correlation
Family History of CVD is uniformly distributedUniform
Patient ID has unique valuesUnique

Reproduction

Analysis started2026-02-15 16:50:41.037687
Analysis finished2026-02-15 16:50:49.488107
Duration8.45 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Patient ID
Text

Unique 

Distinct702
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:49.573073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5.616
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique702 ?
Unique (%)100.0%

Sample

1st rowLNRS9347
2nd rowfqjX3678
3rd rowCCCH7410
4th rowyddj0838
5th rowQpUA6118
ValueCountFrequency (%)
lnrs93471
 
0.1%
fqjx36781
 
0.1%
ccch74101
 
0.1%
yddj08381
 
0.1%
qpua61181
 
0.1%
kqce13661
 
0.1%
oloq34601
 
0.1%
cgwk56811
 
0.1%
osqq70691
 
0.1%
hdlo02621
 
0.1%
Other values (692)692
98.6%
2026-02-15T11:50:49.712389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9296
 
5.3%
8291
 
5.2%
7289
 
5.1%
6288
 
5.1%
1285
 
5.1%
0285
 
5.1%
5279
 
5.0%
2277
 
4.9%
3268
 
4.8%
4250
 
4.5%
Other values (52)2808
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9296
 
5.3%
8291
 
5.2%
7289
 
5.1%
6288
 
5.1%
1285
 
5.1%
0285
 
5.1%
5279
 
5.0%
2277
 
4.9%
3268
 
4.8%
4250
 
4.5%
Other values (52)2808
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9296
 
5.3%
8291
 
5.2%
7289
 
5.1%
6288
 
5.1%
1285
 
5.1%
0285
 
5.1%
5279
 
5.0%
2277
 
4.9%
3268
 
4.8%
4250
 
4.5%
Other values (52)2808
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9296
 
5.3%
8291
 
5.2%
7289
 
5.1%
6288
 
5.1%
1285
 
5.1%
0285
 
5.1%
5279
 
5.0%
2277
 
4.9%
3268
 
4.8%
4250
 
4.5%
Other values (52)2808
50.0%
Distinct587
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
Minimum2020-01-02 00:00:00
Maximum2025-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-15T11:50:49.761235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:49.811936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sex
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
1
357 
0
345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters702
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Length

2026-02-15T11:50:49.861633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:49.886199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Most occurring characters

ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1357
50.9%
0345
49.1%

Age
Real number (ℝ)

Distinct56
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.987826
Minimum6.99
Maximum88.464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:49.918838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.99
5-th percentile30
Q137
median46
Q356
95-th percentile70
Maximum88.464
Range81.474
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.550381
Coefficient of variation (CV)0.26709857
Kurtosis-0.25358917
Mean46.987826
Median Absolute Deviation (MAD)9
Skewness0.44489773
Sum32985.454
Variance157.51207
MonotonicityNot monotonic
2026-02-15T11:50:49.970138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3925
 
3.6%
4824
 
3.4%
4924
 
3.4%
5024
 
3.4%
3324
 
3.4%
3224
 
3.4%
3123
 
3.3%
5622
 
3.1%
3722
 
3.1%
3821
 
3.0%
Other values (46)469
66.8%
ValueCountFrequency (%)
6.991
 
0.1%
255
 
0.7%
265
 
0.7%
276
 
0.9%
284
 
0.6%
296
 
0.9%
3016
2.3%
3123
3.3%
3224
3.4%
3324
3.4%
ValueCountFrequency (%)
88.4641
 
0.1%
795
0.7%
783
 
0.4%
765
0.7%
759
1.3%
742
 
0.3%
733
 
0.4%
725
0.7%
712
 
0.3%
705
0.7%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct591
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.455032
Minimum13.261
Maximum149.877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.016211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.261
5-th percentile52.9078
Q166.66775
median87.65
Q3105.99375
95-th percentile117.295
Maximum149.877
Range136.616
Interquartile range (IQR)39.326

Descriptive statistics

Standard deviation22.089076
Coefficient of variation (CV)0.25549787
Kurtosis-0.98807009
Mean86.455032
Median Absolute Deviation (MAD)19.917
Skewness-0.15370189
Sum60691.432
Variance487.92729
MonotonicityNot monotonic
2026-02-15T11:50:50.062289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.44
 
0.6%
116.24
 
0.6%
663
 
0.4%
52.93
 
0.4%
72.83
 
0.4%
78.43
 
0.4%
118.43
 
0.4%
54.33
 
0.4%
116.53
 
0.4%
51.63
 
0.4%
Other values (581)670
95.4%
ValueCountFrequency (%)
13.2611
0.1%
19.5781
0.1%
21.3161
0.1%
46.650936021
0.1%
50.21
0.1%
50.3071
0.1%
50.3431
0.1%
50.41
0.1%
50.4281
0.1%
50.62
0.3%
ValueCountFrequency (%)
149.8771
0.1%
132.82342471
0.1%
124.36650421
0.1%
121.2005391
0.1%
1201
0.1%
119.91
0.1%
119.5711
0.1%
119.52
0.3%
119.21
0.1%
119.0631
0.1%

Height (m)
Real number (ℝ)

High correlation 

Distinct240
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7558129
Minimum1.1960621
Maximum2.175578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.110349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1960621
5-th percentile1.5633
Q11.66
median1.76
Q31.849645
95-th percentile1.9659
Maximum2.175578
Range0.9795159
Interquartile range (IQR)0.189645

Descriptive statistics

Standard deviation0.1235242
Coefficient of variation (CV)0.070351575
Kurtosis0.34754014
Mean1.7558129
Median Absolute Deviation (MAD)0.09
Skewness-0.032594218
Sum1232.5807
Variance0.015258229
MonotonicityNot monotonic
2026-02-15T11:50:50.160721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8121
 
3.0%
1.8921
 
3.0%
1.6320
 
2.8%
1.6620
 
2.8%
1.7619
 
2.7%
1.8318
 
2.6%
1.6918
 
2.6%
1.8518
 
2.6%
1.717
 
2.4%
1.7317
 
2.4%
Other values (230)513
73.1%
ValueCountFrequency (%)
1.1960621361
0.1%
1.3881
0.1%
1.411
0.1%
1.4634121481
0.1%
1.4909979071
0.1%
1.5031
0.1%
1.5072
0.3%
1.5074072391
0.1%
1.5081
0.1%
1.5091
0.1%
ValueCountFrequency (%)
2.1755780371
0.1%
2.1461
0.1%
2.1391
0.1%
2.1255445981
0.1%
2.111
0.1%
2.0797031351
0.1%
2.0368411321
0.1%
21
0.1%
1.9982
0.3%
1.9953771181
0.1%

BMI
Real number (ℝ)

High correlation 

Distinct486
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.724641
Minimum5.184
Maximum53.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.207157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.184
5-th percentile17.8
Q122.69625
median28.5565
Q334.375
95-th percentile39.9784
Maximum53.028
Range47.844
Interquartile range (IQR)11.67875

Descriptive statistics

Standard deviation7.4897382
Coefficient of variation (CV)0.26074262
Kurtosis-0.44373615
Mean28.724641
Median Absolute Deviation (MAD)5.86
Skewness0.22792941
Sum20164.698
Variance56.096178
MonotonicityNot monotonic
2026-02-15T11:50:50.262923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.26
 
0.9%
19.55
 
0.7%
33.45
 
0.7%
22.85
 
0.7%
31.84
 
0.6%
33.84
 
0.6%
39.14
 
0.6%
20.34
 
0.6%
19.74
 
0.6%
30.84
 
0.6%
Other values (476)657
93.6%
ValueCountFrequency (%)
5.1841
 
0.1%
152
0.3%
15.11
 
0.1%
15.31
 
0.1%
15.42
0.3%
15.61
 
0.1%
15.71
 
0.1%
15.82
0.3%
161
 
0.1%
16.33
0.4%
ValueCountFrequency (%)
53.0281
0.1%
52.741
0.1%
52.1921
0.1%
52.1361
0.1%
51.9841
0.1%
46.11
0.1%
44.81
0.1%
44.71
0.1%
44.21
0.1%
441
0.1%

Abdominal Circumference (cm)
Real number (ℝ)

High correlation 

Distinct529
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.041624
Minimum49.542
Maximum136.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.309245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.542
5-th percentile72.505
Q180.37725
median91.55
Q3102.5
95-th percentile113.0526
Maximum136.336
Range86.794
Interquartile range (IQR)22.12275

Descriptive statistics

Standard deviation13.395558
Coefficient of variation (CV)0.14553804
Kurtosis-0.46310482
Mean92.041624
Median Absolute Deviation (MAD)11.05
Skewness0.27099055
Sum64613.22
Variance179.44097
MonotonicityNot monotonic
2026-02-15T11:50:50.365244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.16
 
0.9%
76.35
 
0.7%
96.65
 
0.7%
79.34
 
0.6%
100.64
 
0.6%
86.94
 
0.6%
75.14
 
0.6%
96.34
 
0.6%
76.74
 
0.6%
78.14
 
0.6%
Other values (519)658
93.7%
ValueCountFrequency (%)
49.5421
 
0.1%
70.0911
 
0.1%
70.11
 
0.1%
70.1841
 
0.1%
70.21
 
0.1%
70.52
0.3%
70.63
0.4%
70.81
 
0.1%
70.91
 
0.1%
70.9021
 
0.1%
ValueCountFrequency (%)
136.3361
0.1%
136.3191
0.1%
133.8461
0.1%
133.0651
0.1%
132.8611
0.1%
119.9961
0.1%
119.8741
0.1%
119.7361
0.1%
119.4951
0.1%
119.4841
0.1%
Distinct648
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.474445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.011396
Min length5

Characters and Unicode

Total characters4.220
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique598 ?
Unique (%)85.2%

Sample

1st row111/76
2nd row118/78
3rd row150/97
4th row122/115
5th row147/90
ValueCountFrequency (%)
111/973
 
0.4%
143/673
 
0.4%
129/613
 
0.4%
126/863
 
0.4%
127/982
 
0.3%
116/692
 
0.3%
136/612
 
0.3%
91/792
 
0.3%
125/732
 
0.3%
134/882
 
0.3%
Other values (638)678
96.6%
2026-02-15T11:50:50.617113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11027
24.3%
/702
16.6%
9373
 
8.8%
6330
 
7.8%
7294
 
7.0%
0290
 
6.9%
8283
 
6.7%
3248
 
5.9%
4237
 
5.6%
2236
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)4220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11027
24.3%
/702
16.6%
9373
 
8.8%
6330
 
7.8%
7294
 
7.0%
0290
 
6.9%
8283
 
6.7%
3248
 
5.9%
4237
 
5.6%
2236
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11027
24.3%
/702
16.6%
9373
 
8.8%
6330
 
7.8%
7294
 
7.0%
0290
 
6.9%
8283
 
6.7%
3248
 
5.9%
4237
 
5.6%
2236
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11027
24.3%
/702
16.6%
9373
 
8.8%
6330
 
7.8%
7294
 
7.0%
0290
 
6.9%
8283
 
6.7%
3248
 
5.9%
4237
 
5.6%
2236
 
5.6%

Total Cholesterol (mg/dL)
Real number (ℝ)

High correlation 

Distinct202
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.05521
Minimum-1.256
Maximum385.679
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size27.1 KiB
2026-02-15T11:50:50.661714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.256
5-th percentile109
Q1150
median196
Q3254
95-th percentile292
Maximum385.679
Range386.935
Interquartile range (IQR)104

Descriptive statistics

Standard deviation61.263534
Coefficient of variation (CV)0.30777157
Kurtosis-0.69599043
Mean199.05521
Median Absolute Deviation (MAD)52
Skewness-0.080024006
Sum139736.76
Variance3753.2206
MonotonicityNot monotonic
2026-02-15T11:50:50.714283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1099
 
1.3%
1799
 
1.3%
2958
 
1.1%
1928
 
1.1%
1327
 
1.0%
2577
 
1.0%
1667
 
1.0%
2567
 
1.0%
1267
 
1.0%
2526
 
0.9%
Other values (192)627
89.3%
ValueCountFrequency (%)
-1.2561
 
0.1%
1.8171
 
0.1%
8.4981
 
0.1%
16.0881
 
0.1%
19.9321
 
0.1%
1006
0.9%
1013
0.4%
1022
 
0.3%
1033
0.4%
1042
 
0.3%
ValueCountFrequency (%)
385.6791
 
0.1%
3002
 
0.3%
2994
0.6%
2983
 
0.4%
2974
0.6%
2966
0.9%
2958
1.1%
2943
 
0.4%
2934
0.6%
2922
 
0.3%

HDL (mg/dL)
Real number (ℝ)

Distinct65
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.823402
Minimum0.612
Maximum110.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.764908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.612
5-th percentile32
Q142
median56
Q369
95-th percentile81
Maximum110.315
Range109.703
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.501534
Coefficient of variation (CV)0.29560244
Kurtosis-0.62959107
Mean55.823402
Median Absolute Deviation (MAD)14
Skewness-0.024720765
Sum39188.028
Variance272.30061
MonotonicityNot monotonic
2026-02-15T11:50:50.817144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6121
 
3.0%
3820
 
2.8%
3619
 
2.7%
4419
 
2.7%
7318
 
2.6%
3017
 
2.4%
4617
 
2.4%
6317
 
2.4%
4817
 
2.4%
3216
 
2.3%
Other values (55)521
74.2%
ValueCountFrequency (%)
0.6121
 
0.1%
1.2761
 
0.1%
6.2831
 
0.1%
7.5421
 
0.1%
3017
2.4%
3112
1.7%
3216
2.3%
3310
1.4%
3416
2.3%
3515
2.1%
ValueCountFrequency (%)
110.3151
 
0.1%
896
0.9%
882
 
0.3%
873
0.4%
863
0.4%
853
0.4%
844
0.6%
835
0.7%
826
0.9%
815
0.7%

Fasting Blood Sugar (mg/dL)
Real number (ℝ)

Distinct127
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.85247
Minimum70
Maximum219.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:50.858663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile74
Q191.25
median115
Q3137
95-th percentile175
Maximum219.667
Range149.667
Interquartile range (IQR)45.75

Descriptive statistics

Standard deviation30.805782
Coefficient of variation (CV)0.2636297
Kurtosis-0.063420774
Mean116.85247
Median Absolute Deviation (MAD)23
Skewness0.57144669
Sum82030.435
Variance948.99622
MonotonicityNot monotonic
2026-02-15T11:50:50.906649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10713
 
1.9%
9313
 
1.9%
12212
 
1.7%
7012
 
1.7%
8911
 
1.6%
11311
 
1.6%
9811
 
1.6%
8811
 
1.6%
15011
 
1.6%
14811
 
1.6%
Other values (117)586
83.5%
ValueCountFrequency (%)
7012
1.7%
715
0.7%
728
1.1%
739
1.3%
748
1.1%
7510
1.4%
767
1.0%
7710
1.4%
787
1.0%
795
0.7%
ValueCountFrequency (%)
219.6671
0.1%
219.1351
0.1%
218.0191
0.1%
215.6141
0.1%
1971
0.1%
1962
0.3%
1952
0.3%
1931
0.1%
1922
0.3%
1912
0.3%

Smoking Status
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
1
373 
0
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters702
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Length

2026-02-15T11:50:50.951940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:50.977270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Most occurring characters

ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1373
53.1%
0329
46.9%

Diabetes Status
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
1
354 
0
348 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters702
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1354
50.4%
0348
49.6%

Length

2026-02-15T11:50:51.013129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:51.042121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1354
50.4%
0348
49.6%

Most occurring characters

ValueCountFrequency (%)
1354
50.4%
0348
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1354
50.4%
0348
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1354
50.4%
0348
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1354
50.4%
0348
49.6%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2
241 
1
232 
0
229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters702
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Length

2026-02-15T11:50:51.074793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:51.100742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Most occurring characters

ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2241
34.3%
1232
33.0%
0229
32.6%

Family History of CVD
Categorical

Uniform 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
1
351 
0
351 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters702
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Length

2026-02-15T11:50:51.135917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:51.159383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Most occurring characters

ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1351
50.0%
0351
50.0%

Waist-to-Height Ratio
Real number (ℝ)

High correlation 

Distinct310
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52620306
Minimum0.267
Maximum0.804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.196751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.267
5-th percentile0.403
Q10.45525
median0.523
Q30.585
95-th percentile0.67395
Maximum0.804
Range0.537
Interquartile range (IQR)0.12975

Descriptive statistics

Standard deviation0.086745609
Coefficient of variation (CV)0.16485197
Kurtosis0.0086914845
Mean0.52620306
Median Absolute Deviation (MAD)0.0635
Skewness0.42541141
Sum369.39455
Variance0.0075248007
MonotonicityNot monotonic
2026-02-15T11:50:51.245887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5617
 
1.0%
0.4237
 
1.0%
0.5787
 
1.0%
0.4757
 
1.0%
0.4846
 
0.9%
0.4126
 
0.9%
0.4316
 
0.9%
0.5526
 
0.9%
0.486
 
0.9%
0.4466
 
0.9%
Other values (300)638
90.9%
ValueCountFrequency (%)
0.2671
0.1%
0.2781
0.1%
0.361
0.1%
0.371
0.1%
0.3761
0.1%
0.3791
0.1%
0.381
0.1%
0.38193449331
0.1%
0.3821
0.1%
0.3842
0.3%
ValueCountFrequency (%)
0.8042
0.3%
0.7872
0.3%
0.7851
0.1%
0.7831
0.1%
0.7821
0.1%
0.781
0.1%
0.7551
0.1%
0.7491
0.1%
0.7391
0.1%
0.7352
0.3%

Systolic BP
Real number (ℝ)

Distinct87
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.86325
Minimum90
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.296016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile93.05
Q1108
median125
Q3142
95-th percentile165.95
Maximum179
Range89
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.772815
Coefficient of variation (CV)0.17298786
Kurtosis-0.57494361
Mean125.86325
Median Absolute Deviation (MAD)17
Skewness0.3440853
Sum88356
Variance474.05545
MonotonicityNot monotonic
2026-02-15T11:50:51.342277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11117
 
2.4%
11316
 
2.3%
14416
 
2.3%
12516
 
2.3%
10115
 
2.1%
10215
 
2.1%
13614
 
2.0%
11614
 
2.0%
13214
 
2.0%
12714
 
2.0%
Other values (77)551
78.5%
ValueCountFrequency (%)
908
1.1%
9112
1.7%
9211
1.6%
935
0.7%
946
0.9%
9511
1.6%
966
0.9%
978
1.1%
9810
1.4%
999
1.3%
ValueCountFrequency (%)
1794
0.6%
1784
0.6%
1773
0.4%
1761
 
0.1%
1754
0.6%
1742
 
0.3%
1733
0.4%
1725
0.7%
1712
 
0.3%
1702
 
0.3%

Diastolic BP
Real number (ℝ)

Distinct59
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.44302
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.386958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61.05
Q171
median83
Q394
95-th percentile113
Maximum119
Range59
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.019684
Coefficient of variation (CV)0.17999928
Kurtosis-0.61401843
Mean83.44302
Median Absolute Deviation (MAD)12
Skewness0.35887064
Sum58577
Variance225.5909
MonotonicityNot monotonic
2026-02-15T11:50:51.440658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8320
 
2.8%
6120
 
2.8%
9519
 
2.7%
6419
 
2.7%
8118
 
2.6%
7818
 
2.6%
6618
 
2.6%
8417
 
2.4%
9717
 
2.4%
9117
 
2.4%
Other values (49)519
73.9%
ValueCountFrequency (%)
6016
2.3%
6120
2.8%
6211
1.6%
6317
2.4%
6419
2.7%
6517
2.4%
6618
2.6%
6710
1.4%
6815
2.1%
6916
2.3%
ValueCountFrequency (%)
1195
0.7%
1186
0.9%
1171
 
0.1%
1164
0.6%
1158
1.1%
1147
1.0%
1137
1.0%
1124
0.6%
1111
 
0.1%
1106
0.9%

Estimated LDL (mg/dL)
Real number (ℝ)

High correlation 

Distinct220
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.28212
Minimum1
Maximum316.071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.491894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.05
Q163
median113
Q3168
95-th percentile208.95
Maximum316.071
Range315.071
Interquartile range (IQR)105

Descriptive statistics

Standard deviation60.938738
Coefficient of variation (CV)0.52860531
Kurtosis-0.8108189
Mean115.28212
Median Absolute Deviation (MAD)52
Skewness0.18936808
Sum80928.045
Variance3713.5298
MonotonicityNot monotonic
2026-02-15T11:50:51.537388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12511
 
1.6%
9010
 
1.4%
399
 
1.3%
1909
 
1.3%
589
 
1.3%
1938
 
1.1%
1788
 
1.1%
1837
 
1.0%
457
 
1.0%
877
 
1.0%
Other values (210)617
87.9%
ValueCountFrequency (%)
14
0.6%
62
0.3%
71
 
0.1%
81
 
0.1%
93
0.4%
102
0.3%
122
0.3%
141
 
0.1%
154
0.6%
161
 
0.1%
ValueCountFrequency (%)
316.0711
0.1%
300.2271
0.1%
298.4921
0.1%
292.2551
0.1%
2371
0.1%
2321
0.1%
2301
0.1%
2292
0.3%
2281
0.1%
2272
0.3%

CVD Risk Score
Real number (ℝ)

High correlation 

Distinct606
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.826138
Minimum10.53
Maximum114.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.584496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.53
5-th percentile13.11815
Q115.2925
median16.996
Q318.895
95-th percentile21.7611
Maximum114.98
Range104.45
Interquartile range (IQR)3.6025

Descriptive statistics

Standard deviation12.431551
Coefficient of variation (CV)0.66033465
Kurtosis40.890308
Mean18.826138
Median Absolute Deviation (MAD)1.806
Skewness6.3448598
Sum13215.949
Variance154.54347
MonotonicityNot monotonic
2026-02-15T11:50:51.633836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.44
 
0.6%
17.74
 
0.6%
17.054
 
0.6%
16.563
 
0.4%
16.023
 
0.4%
15.813
 
0.4%
16.853
 
0.4%
14.163
 
0.4%
18.553
 
0.4%
17.653
 
0.4%
Other values (596)669
95.3%
ValueCountFrequency (%)
10.531
0.1%
10.861
0.1%
10.891
0.1%
11.251
0.1%
11.31
0.1%
11.61
0.1%
11.611
0.1%
11.6331
0.1%
11.741
0.1%
11.8351
0.1%
ValueCountFrequency (%)
114.981
0.1%
114.1431
0.1%
111.0081
0.1%
110.0941
0.1%
104.2711
0.1%
104.0871
0.1%
104.0021
0.1%
99.7751
0.1%
99.3711
0.1%
98.651
0.1%

CVD Risk Level
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
HIGH
340 
INTERMEDIARY
257 
LOW
105 

Length

Max length12
Median length4
Mean length6.7792023
Min length3

Characters and Unicode

Total characters4.759
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIGH
2nd rowINTERMEDIARY
3rd rowHIGH
4th rowINTERMEDIARY
5th rowLOW

Common Values

ValueCountFrequency (%)
HIGH340
48.4%
INTERMEDIARY257
36.6%
LOW105
 
15.0%

Length

2026-02-15T11:50:51.672832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T11:50:51.698584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high340
48.4%
intermediary257
36.6%
low105
 
15.0%

Most occurring characters

ValueCountFrequency (%)
I854
17.9%
H680
14.3%
E514
10.8%
R514
10.8%
G340
 
7.1%
N257
 
5.4%
T257
 
5.4%
M257
 
5.4%
D257
 
5.4%
A257
 
5.4%
Other values (4)572
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I854
17.9%
H680
14.3%
E514
10.8%
R514
10.8%
G340
 
7.1%
N257
 
5.4%
T257
 
5.4%
M257
 
5.4%
D257
 
5.4%
A257
 
5.4%
Other values (4)572
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I854
17.9%
H680
14.3%
E514
10.8%
R514
10.8%
G340
 
7.1%
N257
 
5.4%
T257
 
5.4%
M257
 
5.4%
D257
 
5.4%
A257
 
5.4%
Other values (4)572
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I854
17.9%
H680
14.3%
E514
10.8%
R514
10.8%
G340
 
7.1%
N257
 
5.4%
T257
 
5.4%
M257
 
5.4%
D257
 
5.4%
A257
 
5.4%
Other values (4)572
12.0%

Height (cm)
Real number (ℝ)

High correlation 

Distinct240
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.58129
Minimum119.60621
Maximum217.5578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2026-02-15T11:50:51.735876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum119.60621
5-th percentile156.33
Q1166
median176
Q3184.9645
95-th percentile196.59
Maximum217.5578
Range97.95159
Interquartile range (IQR)18.9645

Descriptive statistics

Standard deviation12.35242
Coefficient of variation (CV)0.070351575
Kurtosis0.34754014
Mean175.58129
Median Absolute Deviation (MAD)9
Skewness-0.032594218
Sum123258.07
Variance152.58229
MonotonicityNot monotonic
2026-02-15T11:50:51.784899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18121
 
3.0%
18921
 
3.0%
16320
 
2.8%
16620
 
2.8%
17619
 
2.7%
18318
 
2.6%
16918
 
2.6%
18518
 
2.6%
17017
 
2.4%
17317
 
2.4%
Other values (230)513
73.1%
ValueCountFrequency (%)
119.60621361
0.1%
138.81
0.1%
1411
0.1%
146.34121481
0.1%
149.09979071
0.1%
150.31
0.1%
150.72
0.3%
150.74072391
0.1%
150.81
0.1%
150.91
0.1%
ValueCountFrequency (%)
217.55780371
0.1%
214.61
0.1%
213.91
0.1%
212.55445981
0.1%
2111
0.1%
207.97031351
0.1%
203.68411321
0.1%
2001
0.1%
199.82
0.3%
199.53771181
0.1%

Interactions

2026-02-15T11:50:48.753155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-15T11:50:44.323796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:44.879511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:45.411842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:45.965452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:46.498272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:47.007187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:47.552306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:48.205274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T11:50:48.718765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-15T11:50:51.845744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Abdominal Circumference (cm)AgeBMICVD Risk LevelCVD Risk ScoreDiabetes StatusDiastolic BPEstimated LDL (mg/dL)Family History of CVDFasting Blood Sugar (mg/dL)HDL (mg/dL)Height (cm)Height (m)Physical Activity LevelSexSmoking StatusSystolic BPTotal Cholesterol (mg/dL)Waist-to-Height RatioWeight (kg)
Abdominal Circumference (cm)1.0000.0800.0460.0510.1160.077-0.0010.0650.0280.016-0.022-0.013-0.0130.0230.0000.0000.0780.0560.8750.065
Age0.0801.0000.0170.1480.0290.0730.037-0.0170.0230.0610.0350.0520.0520.0000.0000.0000.027-0.0040.0380.039
BMI0.0460.0171.0000.1400.5950.0000.0520.0390.0000.050-0.018-0.162-0.1620.0000.0580.0000.0110.0240.1100.621
CVD Risk Level0.0510.1480.1401.0000.0700.1570.1310.1380.1760.1840.1390.1000.1000.1390.0000.2340.1540.1360.0370.098
CVD Risk Score0.1160.0290.5950.0701.0000.1750.1160.4360.0750.0810.047-0.054-0.0540.0000.0000.0000.4100.4530.1340.397
Diabetes Status0.0770.0730.0000.1570.1751.0000.0000.0000.0200.0000.0000.0000.0000.0920.0000.0000.0000.0720.0000.086
Diastolic BP-0.0010.0370.0520.1310.1160.0001.0000.1080.0550.080-0.0080.0060.0060.0000.0000.0650.0460.113-0.0100.052
Estimated LDL (mg/dL)0.065-0.0170.0390.1380.4360.0000.1081.0000.043-0.005-0.1600.0340.0340.0320.0350.000-0.0130.9290.0350.005
Family History of CVD0.0280.0230.0000.1760.0750.0200.0550.0431.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0310.000
Fasting Blood Sugar (mg/dL)0.0160.0610.0500.1840.0810.0000.080-0.0050.0871.0000.0430.0230.0230.0400.0000.0000.0780.003-0.0020.066
HDL (mg/dL)-0.0220.035-0.0180.1390.0470.000-0.008-0.1600.0000.0431.0000.0240.0240.0360.0710.0850.0920.093-0.0240.023
Height (cm)-0.0130.052-0.1620.100-0.0540.0000.0060.0340.0000.0230.0241.0001.0000.0000.0520.0000.0170.043-0.3740.056
Height (m)-0.0130.052-0.1620.100-0.0540.0000.0060.0340.0000.0230.0241.0001.0000.0000.0520.0000.0170.043-0.3740.056
Physical Activity Level0.0230.0000.0000.1390.0000.0920.0000.0320.0000.0400.0360.0000.0001.0000.0000.0000.0000.0520.0000.018
Sex0.0000.0000.0580.0000.0000.0000.0000.0350.0000.0000.0710.0520.0520.0001.0000.0240.0000.0000.0630.052
Smoking Status0.0000.0000.0000.2340.0000.0000.0650.0000.0000.0000.0850.0000.0000.0000.0241.0000.0000.0000.0000.000
Systolic BP0.0780.0270.0110.1540.4100.0000.046-0.0130.0000.0780.0920.0170.0170.0000.0000.0001.0000.0140.0490.021
Total Cholesterol (mg/dL)0.056-0.0040.0240.1360.4530.0720.1130.9290.0000.0030.0930.0430.0430.0520.0000.0000.0141.0000.0260.007
Waist-to-Height Ratio0.8750.0380.1100.0370.1340.000-0.0100.0350.031-0.002-0.024-0.374-0.3740.0000.0630.0000.0490.0261.0000.055
Weight (kg)0.0650.0390.6210.0980.3970.0860.0520.0050.0000.0660.0230.0560.0560.0180.0520.0000.0210.0070.0551.000

Missing values

2026-02-15T11:50:49.336474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-15T11:50:49.435992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDWaist-to-Height RatioSystolic BPDiastolic BPEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk LevelHeight (cm)
261LNRS93472020-10-02039.088.7000001.62033.800109.200111/76285.053.0120.001010.67411176202.020.010HIGH162.0
900fqjX36782021-04-05037.0116.5350001.81228.50276.302118/78143.061.0145.001110.4211187852.016.460INTERMEDIARY181.2
474CCCH741028 Oct 22157.0104.8000001.84031.000104.600150/97192.078.0106.011210.5681509784.019.540HIGH184.0
503yddj0838February 17, 2023154.053.9671751.58021.618104.076122/115117.051.097.010110.65912211536.012.764INTERMEDIARY158.0
837QpUA611803-24-2024040.063.6570001.97938.797105.433147/90213.034.0138.001100.53314790149.0104.002LOW197.9
481KqCE136603 Jan 24056.0111.6000001.81034.10086.000102/100161.069.0145.011100.47510210062.017.140HIGH181.0
64Oloq3460April 15, 2025137.092.9000001.87026.60071.800113/79115.079.0125.000210.384113796.013.270INTERMEDIARY187.0
380CGWK5681April 13, 2021162.0103.3700001.57137.72894.124134/110273.038.0177.011110.599134110205.021.706LOW157.1
1176oSqq706918 Jun 23138.068.5160001.92939.96796.957105/84241.052.0103.010110.50310584159.018.063HIGH192.9
688HDlO026212-15-2022058.050.3070001.63328.20675.421122/119260.062.0124.011200.462122119168.018.941LOW163.3
Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDWaist-to-Height RatioSystolic BPDiastolic BPEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk LevelHeight (cm)
136wBZn811624 Jun 24150.063.4001.63023.90000076.3000125/87141.039.00088.010010.4681258772.013.850HIGH163.0
691ZbGp3615September 05, 2024128.0116.0721.50819.61300094.576097/101143.050.000120.000000.6279710163.011.633LOW150.8
22doLN1732February 18, 2021027.0114.3121.68825.16300098.5792140/106210.041.00080.010000.584140106139.016.233INTERMEDIARY168.8
785lSgT5725August 30, 2022079.0118.5441.59127.88100091.3880179/74215.036.000115.011110.57417974149.020.826LOW159.1
78JsXM524013/04/2022029.080.4281.86538.70900082.2870151/93108.059.000121.010110.4411519319.017.452HIGH186.5
121rxiy39832021-07-06132.071.7001.72024.200000109.9000150/84170.038.00092.010110.63915084102.015.740INTERMEDIARY172.0
309AUYM59702024-09-24033.088.4001.86025.60000090.1000116/69268.068.00088.010000.48411669170.016.280HIGH186.0
1024NPoi035805-30-2025158.098.3001.90027.22991786.9000112/84168.074.00078.000210.4571128464.014.400INTERMEDIARY190.0
491fLBK9308March 31, 2025053.068.7641.74234.99400094.3540156/64261.043.00078.000110.54215664188.020.019LOW174.2
117qYlm867411-30-2024036.083.7001.83025.00000085.6000136/60117.07.54286.010200.4681366057.014.140INTERMEDIARY183.0